Search results for: production scheduling optimization

Authors: Efrain Rodriguez, Sergio Pertuz, Cristhian Riano

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Tool-path generation is an essential step in the FFF (Fused Filament Fabrication)-based Additive Manufacturing (AM) process planning. In the manufacture of a mechanical part by using additive processes, high resource consumption and prolonged production times are inherent drawbacks of these processes mainly due to non-optimized tool-path generation. In this work, we propose a heuristic-search intelligent algorithm-based approach for optimized tool-path generation for FFF-based AM. The main benefit of this approach is a significant reduction of travels without material deposition when the AM machine performs moves without any extrusion. The optimization method used reduces the number of travels without extrusion in comparison with commercial software as Slic3r or Cura Engine, which means a reduction of production time.

Keywords: additive manufacturing, tool-path optimization, fused filament fabrication, process planning

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Authors: Pablo Ignacio Hernández Arango, Niels Lindemeyer

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Chlorine, as Sodium Hypochlorite (NaClO) solution in water, is an effective, worldwide spread, and economical substance to eliminate germs in the water. The disinfection potential of chlorine lies in its ability to degrade the outer surfaces of bacterial cells and viruses. This contribution reports the main parameters of the brine electrolysis for the production of NaClO, which is afterward used for the disinfection of water either for drinking or recreative uses. Herein, the system design was simulated, optimized, build, and tested based on titanium electrodes. The process optimization considers the whole process, from the salt (NaCl) dilution tank in order to maximize its operation time util the electrolysis itself in order to maximize the chlorine production reducing the energy and raw material (salt and water) consumption. One novel idea behind this optimization process is the modification of the flow pattern inside the electrochemical reactors. The increasing turbulence and residence time impact positively the operations figures. The operational parameters, which are defined in this study were compared and benchmarked with the parameters of actual commercial systems in order to validate the pertinency of those results.

Keywords: electrolysis, water disinfection, sodium hypochlorite, process optimization

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Authors: Bhrugesh Radadiya, Jaydeep Shah

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In Indian foundry industry, there is a need of defect free casting with minimum production cost in short lead time. Casting defect is a very large issue in foundry shop which increases the rejection rate of casting and wastage of materials. The various parameters influences on casting process such as mold machine related parameters, green sand related parameters, cast metal related parameters, mold related parameters and shake out related parameters. The mold related parameters are most influences on casting defects in sand casting process. This paper review the casting produced by foundry with shrinkage and blow holes as a major defects was analyzed and identified that mold related parameters such as mold temperature, pouring temperature and runner size were not properly set in sand casting process. These parameters were optimized using different optimization techniques such as Taguchi method, Response surface methodology, Genetic algorithm and Teaching-learning based optimization algorithm. Finally, concluded that a Teaching-learning based optimization algorithm give better result than other optimization techniques.

Keywords: casting defects, genetic algorithm, parametric optimization, Taguchi method, TLBO algorithm

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Authors: Wanqing You, Kai Qian, Ying Qian

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The concepts of Cloud Computing provide users with infrastructure, platform and software as service, which make those services more accessible for people via Internet. To better analysis the performance of Cloud Computing provisioning policies as well as resources allocation strategies, a toolkit named CloudSim proposed. With CloudSim, the Cloud Computing environment can be easily constructed by modelling and simulating cloud computing components, such as datacenter, host, and virtual machine. A good scheduling strategy is the key to achieve the load balancing among different machines as well as to improve the utilization of basic resources. Recently, the existing scheduling algorithms may work well in some presumptive cases in a single machine; however they are unable to make the best decision for the unforeseen future. In real world scenario, there would be numbers of tasks as well as several virtual machines working in parallel. Based on the concepts of multi-queue, this paper presents a new scheduling algorithm to schedule tasks with CloudSim by taking into account several parameters, the machines’ capacity, the priority of tasks and the history log.

Keywords: hierarchical queue, load balancing, CloudSim, information technology

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Authors: Serges Mendomo Meye, Li Guowei, Shen Zhenzhong, Gan Lei, Xu Liqun

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Given the limitations of the original whale optimization algorithm (WAO) in local optimum and low convergence accuracy in truss structure optimization problems, based on the fundamental whale algorithm, an improved whale optimization algorithm (SWAO) based on information entropy is proposed. The information entropy itself is an uncertain measure. It is used to control the range of whale searches in path selection. It can overcome the shortcomings of the basic whale optimization algorithm (WAO) and can improve the global convergence speed of the algorithm. Taking truss structure as the optimization research object, the mathematical model of truss structure optimization is established; the cross-sectional area of truss is taken as the design variable; the objective function is the weight of truss structure; and an improved whale optimization algorithm (SWAO) is used for optimization design, which provides a new idea and means for its application in large and complex engineering structure optimization design.

Keywords: information entropy, structural optimization, truss structure, whale algorithm

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Authors: R. Rajabpour, N. Talebbeydokhti, M. H. Ahmadi

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Carefully scheduling the operations of pumps can be resulted to significant energy savings. Schedules can be defined either implicit, in terms of other elements of the network such as tank levels, or explicit by specifying the time during which each pump is on/off. In this study, two new explicit representations based on time-controlled triggers were analyzed, where the maximum number of pump switches was established beforehand, and the schedule may contain fewer switches than the maximum. The optimal operation of pumping stations was determined using a Jumping Particle Swarm Optimization (JPSO) algorithm to achieve the minimum energy cost. The model integrates JPSO optimizer and EPANET hydraulic network solver. The optimal pump operation schedule of VanZyl water distribution system was determined using the proposed model and compared with those from Genetic and Ant Colony algorithms. The results indicate that the proposed model utilizing the JPSP algorithm outperformed the others and is a versatile management model for the operation of real-world water distribution system.

Keywords: JPSO, operation, optimization, water distribution system

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Authors: Jasbir Singh Gill, Baljit Singh

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Scheduling and mapping the application task graph on multiprocessor parallel systems is considered as the most crucial and critical NP-complete problem. Many genetic algorithms have been proposed to solve such problems. In this paper, two genetic approach based algorithms have been designed and developed with or without task duplication. The proposed algorithms work on two fitness functions. The first fitness i.e. task fitness is used to minimize the total finish time of the schedule (schedule length) while the second fitness function i.e. process fitness is concerned with allocating the tasks to the available highly efficient processor from the list of available processors (load balance). Proposed genetic-based algorithms have been experimentally implemented and evaluated with other state-of-art popular and widely used algorithms.

Keywords: parallel computing, task scheduling, task duplication, genetic algorithm

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Authors: Ramin Javadzadeh

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The particle swarm optimization are Meta heuristic optimization method, which are used for clustering and pattern recognition applications are abundantly. These algorithms in multimodal optimization problems are more efficient than genetic algorithms. A major drawback in these algorithms is their slow convergence to global optimum and their weak stability can be considered in various running of these algorithms. In this paper, improved Particle swarm optimization is introduced for the first time to overcome its problems. The fuzzy cellular automata is used for improving the algorithm efficiently. The credibility of the proposed approach is evaluated by simulations, and it is shown that the proposed approach achieves better results can be achieved compared to the Particle swarm optimization algorithms.

Keywords: cellular automata, cellular learning automata, local search, optimization, particle swarm optimization

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Authors: María L. Jalón, Feargal Brennan

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A non-stationary stochastic optimization methodology is applied to an OWC (oscillating water column) to find the design that maximizes the wave energy extraction. Different temporal cycles are considered to represent the long-term variability of the wave climate at the site in the optimization problem. The results of the non-stationary stochastic optimization problem are compared against those obtained by a stationary stochastic optimization problem. The comparative analysis reveals that the proposed non-stationary optimization provides designs with a better fit to reality. However, the stationarity assumption can be adequate when looking at averaged system response.

Keywords: non-stationary stochastic optimization, oscillating water, temporal variability, wave energy

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Authors: Liudmyla Koliechkina, Olena Dvirna

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The statement of the multi-objective optimization problem on combinatorial configurations is formulated, and the approach to its solution is proposed. The problem is of interest as a combinatorial optimization one with many criteria, which is a model of many applied tasks. The approach to solving the multi-objective optimization problem on combinatorial configurations consists of two stages; the first is the reduction of the multi-objective problem to the single criterion based on existing multi-objective optimization methods, the second stage solves the directly replaced single criterion combinatorial optimization problem by the horizontal combinatorial method. This approach provides the optimal solution to the multi-objective optimization problem on combinatorial configurations, taking into account additional restrictions for a finite number of steps.

Keywords: discrete set, linear combinatorial optimization, multi-objective optimization, Pareto solutions, partial permutation set, structural graph

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Authors: Shuen-Tai Wang, Yu-Ching Lin

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With more and more workflow systems adopting cloud as their execution environment, it presents various challenges that need to be addressed in order to be utilized efficiently. This paper introduces a method for resource provisioning based on our previous research of dynamic allocation and its pipeline processes. We present an abstraction for workload scheduling in which independent tasks get scheduled among various available processors of distributed computing for optimization. We also propose an integrated web-based workflow designer by taking advantage of the HTML5 technology and chaining together multiple tools. In order to make the combination of multiple pipelines executing on the cloud in parallel, we develop a script translator and an execution engine for workflow management in the cloud. All information is known in advance by the workflow engine and tasks are allocated according to the prior knowledge in the repository. This proposed effort has the potential to provide support for process definition, workflow enactment and monitoring of workflow processes. Users would benefit from the web-based system that allows creation and execution of pipelines without scripting knowledge.

Keywords: workflow systems, resources provisioning, workload scheduling, web-based, workflow engine

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Authors: Vivek Upadhayay, Siddharth Deshmukh

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In recent years utilization of renewable energy sources has increased majorly because of the increase in global warming concerns. Organization these days are generally operated by Micro grid or smart grid on a small level. Power optimization and optimal load tripping is possible in a smart grid based industry. In any plant or industry loads can be divided into different categories based on their importance to the plant and power requirement pattern in the working days. Coming up with an idea to divide loads in different such categories and providing different power management algorithm to each category of load can reduce the power cost and can come handy in balancing stability and reliability of power. An objective function is defined which is subjected to a variable that we are supposed to minimize. Constraint equations are formed taking difference between the power usages pattern of present day and same day of previous week. By considering the objectives of minimal load tripping and optimal power distribution the proposed problem formulation is a multi-object optimization problem. Through normalization of each objective function, the multi-objective optimization is transformed to single-objective optimization. As a result we are getting the optimized values of power required to each load for present day by use of the past values of the required power for the same day of last week. It is quite a demand response scheduling of power. These minimized values then will be distributed to each load through an algorithm used to optimize the power distribution at a greater depth. In case of power storage exceeding the power requirement, profit can be made by selling exceeding power to the main grid.

Keywords: power flow optimization, power trading enhancement, smart grid, multi-object optimization

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Authors: Salam Saudagar, Ankit Kamboj, Niraj Mohan, Satgounda Patil, Nilesh Powar

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Task Assignment and Scheduling is a challenging Operations Research problem when there is a limited number of resources and comparatively higher number of tasks. The Cost Management team at Cummins needs to assign tasks based on a deadline and must prioritize some of the tasks as per business requirements. Moreover, there is a constraint on the resources that assignment of tasks should be done based on an individual skill level, that may vary for different tasks. Another constraint is for scheduling the tasks that should be evenly distributed in terms of number of working hours, which adds further complexity to this problem. The proposed greedy approach to solve assignment and scheduling problem first assigns the task based on management priority and then by the closest deadline. This is followed by an iterative selection of an available resource with the least allocated total working hours for a task, i.e. finding the local optimal choice for each task with the goal of determining the global optimum. The greedy approach task allocation is compared with a variant of Hungarian Algorithm, and it is observed that the proposed approach gives an equal allocation of working hours among the resources. The comparative study of the proposed approach is also done with manual task allocation and it is noted that the visibility of the task timeline has increased from 2 months to 6 months. An interactive dashboard app is created for the greedy assignment and scheduling approach and the tasks with more than 2 months horizon that were waiting in a queue without a delivery date initially are now analyzed effectively by the business with expected timelines for completion.

Keywords: assignment, deadline, greedy approach, Hungarian algorithm, operations research, scheduling

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Authors: Imen Amari, Kaouther Gasmi, Asma Rebaya, Salem Hasnaoui

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The problem of scheduling and mapping data flow applications on multi-core architectures is notoriously difficult. This difficulty is related to the rapid evaluation of Telecommunication and multimedia systems accompanied by a rapid increase of user requirements in terms of latency, execution time, consumption, energy, etc. Having an optimal scheduling on multi-cores DSP (Digital signal Processors) platforms is a challenging task. In this context, we present a novel technic and algorithm in order to find a valid schedule that optimizes the key performance metrics particularly the Latency. Our contribution is based on Satisfiability Modulo Theories (SMT) solving technologies which is strongly driven by the industrial applications and needs. This paper, describe a scheduling module integrated in our proposed Workflow which is advised to be a successful approach for programming the applications based on NoC-DSP platforms. This workflow transform automatically a Simulink model to a synchronous dataflow (SDF) model. The automatic transformation followed by SMT solver scheduling aim to minimize the final latency and other software/hardware metrics in terms of an optimal schedule. Also, finding the optimal numbers of cores to be used. In fact, our proposed workflow taking as entry point a Simulink file (.mdl or .slx) derived from embedded Matlab functions. We use an approach which is based on the synchronous and hierarchical behavior of both Simulink and SDF. Whence, results of running the scheduler which exist in the Workflow mentioned above using our proposed SMT solver algorithm refinements produce the best possible scheduling in terms of latency and numbers of cores.

Keywords: multi-cores DSP, scheduling, SMT solver, workflow

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Authors: R. M. Rizk-Allah

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This paper presents co-evolutionary fruit fly optimization algorithm based on firefly algorithm (CFOA-FA) for solving unconstrained optimization problems. The proposed algorithm integrates the merits of fruit fly optimization algorithm (FOA), firefly algorithm (FA) and elite strategy to refine the performance of classical FOA. Moreover, co-evolutionary mechanism is performed by applying FA procedures to ensure the diversity of the swarm. Finally, the proposed algorithm CFOA- FA is tested on several benchmark problems from the usual literature and the numerical results have demonstrated the superiority of the proposed algorithm for finding the global optimal solution.

Keywords: firefly algorithm, fruit fly optimization algorithm, unconstrained optimization problems

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Authors: Sang Guen Kim, Sib Sankar Giri, Jin Woo Jun, Saekil Yun, Hyoun Joong Kim, Sang Wha Kim, Jung Woo Kang, Se Jin Han, Se Chang Park

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Emerging of the super bacteria, bacteriophages are considered to be as an alternative to antibiotics. As the demand of phage increased, economical and large production of phage is becoming one of the critical points. For the therapeutic use, what is important is to eradicate the pathogenic bacteria as fast as possible, so higher concentration of phages is generally needed for effective therapeutic function. On the contrary, for the maximum production, bacteria work as a phage producing factory. As a microbial cell factory, bacteria is needed to last longer producing the phages without eradication. Consequently, killing the bacteria fast has a negative effect on large production. In this study, Multiplicity of Infection (MOI) was manipulated based on initial bacterial inoculation and used phage pAh-6C which has therapeutic effect against Aeromonas hydrophila. 1, 5 and 10 percent of overnight bacterial culture was inoculated and each bacterial culture was co-cultured with the phage of which MOI of 0.01, 0.0001, and 0.000001 respectively. Simply changing the initial MOI as well as bacterial inoculation concentration has regulated the production quantity of the phage without any other changes to culture conditions. It is anticipated that this result can be used as a foundational data for mass production of lytic bacteriophages which can be used as the therapeutic bio-control agent.

Keywords: bacteriophage, multiplicity of infection, optimization, Aeromonas hydrophila

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Authors: Refal Hussain, Saifuddin M. Nomanbhay

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Glycerol is a valuable raw material for the production of industrially useful metabolites. Among many promising applications for the use of glycerol is its bioconversion to high value-added compounds, such as bioethanol through microbial fermentation. Bioethanol is an important industrial chemical with emerging potential as a biofuel to replace vanishing fossil fuels. The yield of liquid fuel in this process was greatly influenced by various parameters viz, temperature, pH, glycerol concentration, organic concentration, and agitation speed were considered. The present study was undertaken to investigate optimum parameters for bioethanol production from raw glycerol by immobilized mutant Escherichia coli (E.coli) (ATCC11505) strain on chitosan cross linked glutaraldehyde optimized by Taguchi statistical method in shake flasks. The initial parameters were set each at four levels and the orthogonal array layout of L16 (45) conducted. The important controlling parameters for optimized the operational fermentation was temperature 38 °C, medium pH 6.5, initial glycerol concentration (250 g/l), and organic source concentration (5 g/l). Fermentation with optimized parameters was carried out in a custom fabricated shake flask. The predicted value of bioethanol production under optimized conditions was (118.13 g/l). Immobilized cells are mainly used for economic benefits of continuous production or repeated use in continuous as well as in batch mode.

Keywords: bioethanol, Escherichia coli, immobilization, optimization

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Authors: Hassen Hamouda, Mohamed Ouwais Kabaou, Med Salim Bouhlel

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The growth of wireless technology made opportunistic scheduling a widespread theme in recent research. Providing high system throughput without reducing fairness allocation is becoming a very challenging task. A suitable policy for resource allocation among users is of crucial importance. This study focuses on scheduling multiple streaming flows on the downlink of a WiMAX system based on orthogonal frequency division multiple access (OFDMA). In this paper, we take the first step in formulating and analyzing this problem scrupulously. As a result, we proposed a new scheduling scheme based on Round Robin (RR) Algorithm. Because of its non-opportunistic process, RR does not take in account radio conditions and consequently it affect both system throughput and multi-users diversity. Our contribution called MORRA (Modified Round Robin Opportunistic Algorithm) consists to propose a solution to this issue. MORRA not only exploits the concept of opportunistic scheduler but also takes into account other parameters in the allocation process. The first parameter is called courtesy coefficient (CC) and the second is called Buffer Occupancy (BO). Performance evaluation shows that this well-balanced scheme outperforms both RR and MaxSNR schedulers and demonstrate that choosing between system throughput and fairness is not required.

Keywords: OFDMA, opportunistic scheduling, fairness hierarchy, courtesy coefficient, buffer occupancy

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Authors: Ashish Shukla, K. P. Mishra, Pushplata Tripathi

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This paper investigates the production and optimization of β-galactosidase enzyme using synthetic medium by isolated wild strains (S1, S2) mutated strains (M1, M2) through SSF and SmF. Among the different cell disintegration methods used, the highest specific activity was obtained when the cells were permeabilized using isoamyl alcohol. Wet lab experiments were performed to investigate the effects of carbon and nitrogen substrates present in Vogel’s medium on β-galactosidase enzyme activity using S1, S2, and M1, M2 strains through SSF. SmF experiments were performed for effects of carbon and nitrogen sources in YLK2Mg medium on β-galactosidase enzyme activity using S1, S2 and M1, M2 strains. Effect of pH on β-galactosidase enzyme production was also done using S1, S2, and M1, M2 strains. Results were found to be very appreciable in all the cases.

Keywords: β-galactosidase, cell disintegration, permeabilized, SSF, SmF

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Authors: Pham Van Nha, Le Cam Binh

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Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classification. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes.

Keywords: analysis of optimization, artificial intelligence based optimization, optimization for learning and data analysis, global optimization

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Authors: Swarali Hingse, Shraddha Digole, Uday Annapure

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The present study investigates the biotransformation of ferulic acid to vanillin by Pycnoporus cinnabarinus and its optimization using one-factor-at-a-time method as well as statistical approach. Effect of various physicochemical parameters and medium components was studied using one-factor-at-a-time method. Screening of the significant factors was carried out using L25 Taguchi orthogonal array and then these selected significant factors were further optimized using response surface methodology (RSM). Significant media components obtained using Taguchi L25 orthogonal array were glucose, KH2PO4 and yeast extract. Further, a Box Behnken design was used to investigate the interactive effects of the three most significant media components. The final medium obtained after optimization using RSM containing glucose (34.89 g/L), diammonium tartrate (1 g/L), yeast extract (1.47 g/L), MgSO4•7H2O (0.5 g/L), KH2PO4 (0.15 g/L), and CaCl2•2H2O (20 mg/L) resulted in amplification of vanillin production from 30.88 mg/L to 187.63 mg/L.

Keywords: ferulic acid, pycnoporus cinnabarinus, response surface methodology, vanillin

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Authors: A. W. Zularisam, Lakhveer Singh, Mimi Sakinah Abdul Munaim

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In this study, the optimization of hydrogen production using polyethylene glycol (PEG) immobilized sludge was investigated in batch tests. Palm oil mill effluent (POME) is used as a substrate that can act as a carbon source. Experiment focus on the effect of some important affecting factors on fermentative hydrogen production. Results showed that immobilized sludge demonstrated the maximum hydrogen production rate of 340 mL/L-POME/h under follow optimal condition: amount of biomass 10 mg VSS/ g bead, PEG concentration 10%, and cell age 24 h or 40 h. More importantly, immobilized sludge not only enhanced hydrogen production but can also tolerate the harsh environment and produce hydrogen at the wide ranges of pH. The present results indicate the potential of PEG-immobilized sludge for large-scale operations as well; these factors play an important role in stable and continuous hydrogen production.

Keywords: bioydrogen, immobilization, polyethylene glycol, palm oil mill effluent, dark fermentation

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Authors: E. Koyuncu

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The problem of Order Acceptance and Scheduling (OAS) is defined as a joint decision of which orders to accept for processing and how to schedule them. Any linear programming model representing real-world situation involves the parameters defined by the decision maker in an uncertain way or by means of language statement. Fuzzy data can be used to incorporate vagueness in the real-life situation. In this study, a fuzzy mathematical model is proposed for a single machine OAS problem, where the orders are defined by their fuzzy due dates, fuzzy processing times, and fuzzy sequence dependent setup times. The signed distance method, one of the fuzzy ranking methods, is used to handle the fuzzy constraints in the model.

Keywords: fuzzy mathematical programming, fuzzy ranking, order acceptance, single machine scheduling

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Authors: Sait Ali Uymaz, Gülay Tezel

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This paper presents the comparison results on the performance of the Cuckoo Search (CS) algorithm for constrained optimization problems. For constraint handling, CS algorithm uses penalty method. CS algorithm is tested on thirteen well-known test problems and the results obtained are compared to Particle Swarm Optimization (PSO) algorithm. Mean, best, median and worst values were employed for the analyses of performance.

Keywords: cuckoo search, particle swarm optimization, constrained optimization problems, penalty method

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Authors: Z. Babic, I. Veza, A. Balic, M. Crnjac

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The linear programming model is sometimes difficult to apply in real business situations due to its assumption of proportionality. This paper shows an example of how to use De Novo programming approach instead of linear programming. In the De Novo programming, resources are not fixed like in linear programming but resource quantities depend only on available budget. Budget is a new, important element of the De Novo approach. Two different production situations are presented: increasing costs and quantity discounts of raw materials. The focus of this paper is on advantages of the De Novo approach in the optimization of production plan for production company which produces souvenirs made from famous stone from the island of Brac, one of the greatest islands from Croatia.

Keywords: business process, De Novo programming, optimizing, production

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Authors: Ganiyat Soliu, Glen Bright, Chiemela Onunka

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Performance optimization plays a key role in controlling the waiting time during manufacturing in an advanced manufacturing environment to improve productivity. Queuing mathematical modeling theory was used to examine the performance of the multi-stage production line. Robotics as a disruptive technology was implemented into a virtual manufacturing scenario during the packaging process to study the effect of waiting time on productivity. The queuing mathematical model was used to determine the optimum service rate required by robots during the packaging stage of manufacturing to yield an optimum production cost. Different rates of production were assumed in a virtual manufacturing environment, cost of packaging was estimated with optimum production cost. An equation was generated using queuing mathematical modeling theory and the theorem adopted for analysis of the scenario is the Newton Raphson theorem. Queuing theory presented here provides an adequate analysis of the number of robots required to regulate waiting time in order to increase the number of output. Arrival rate of the product was fast which shows that queuing mathematical model was effective in minimizing service cost and the waiting time during manufacturing. At a reduced waiting time, there was an improvement in the number of products obtained per hour. The overall productivity was improved based on the assumptions used in the queuing modeling theory implemented in the virtual manufacturing scenario.

Keywords: performance optimization, productivity, queuing theory, robotics

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Authors: Idan Chiyanzu, Maruping Mangena

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Crude glycerol, a major by-product from the transesterification of triacylglycerides with alcohol to biodiesel, is known to have a broad range of applications. For example, its bioconversion can afford a wide range of chemicals including alcohols, organic acids, hydrogen, solvents and intermediate compounds. In bacteria, the 2-oxoglutarate dioxygenase (2-OGD) enzymes are widely found among the Pseudomonas syringae species and have been recognized with an emerging importance in ethylene formation. However, the use of optimized enzyme function in recombinant systems for crude glycerol conversion to ethylene is still not been reported. The present study investigated the production of ethylene from crude glycerol using engineered E. coli MG1655 and JM109 strains. Ethylene production with an optimized expression system for 2-OGD in E. coli using a codon optimized construct of the ethylene-forming gene was studied. The codon-optimization resulted in a 20-fold increase of protein production and thus an enhanced production of the ethylene gas. For a reliable bioreactor performance, the effect of temperature, fermentation time, pH, substrate concentration, the concentration of methanol, concentration of potassium hydroxide and media supplements on ethylene yield was investigated. The results demonstrate that the recombinant enzyme can be used for future studies to exploit the conversion of low-priced crude glycerol into advanced value products like light olefins, and tools including recombineering techniques for DNA, molecular biology, and bioengineering can be used to allowing unlimited the production of ethylene directly from the fermentation of crude glycerol. It can be concluded that recombinant E.coli production systems represent significantly secure, renewable and environmentally safe alternative to thermochemical approach to ethylene production.

Keywords: crude glycerol, bioethylene, recombinant E. coli, optimization

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Authors: N. D. Iswarya, M. A. Maluk Mohamed, N. Vijaya

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Grid is infrastructure that allows the deployment of distributed data in large size from multiple locations to reach a common goal. Scheduling data intensive applications becomes challenging as the size of data sets are very huge in size. Only two solutions exist in order to tackle this challenging issue. First, computation which requires huge data sets to be processed can be transferred to the data site. Second, the required data sets can be transferred to the computation site. In the former scenario, the computation cannot be transferred since the servers are storage/data servers with little or no computational capability. Hence, the second scenario can be considered for further exploration. During scheduling, transferring huge data sets from one site to another site requires more network bandwidth. In order to mitigate this issue, this work focuses on incorporating cognitive science in scheduling. Cognitive Science is the study of human brain and its related activities. Current researches are mainly focused on to incorporate cognitive science in various computational modeling techniques. In this work, the problem solving approach of human brain is studied and incorporated during the data intensive scheduling in grid environments. Here, a cognitive engine is designed and deployed in various grid sites. The intelligent agents present in CE will help in analyzing the request and creating the knowledge base. Depending upon the link capacity, decision will be taken whether to transfer data sets or to partition the data sets. Prediction of next request is made by the agents to serve the requesting site with data sets in advance. This will reduce the data availability time and data transfer time. Replica catalog and Meta data catalog created by the agents assist in decision making process.

Keywords: data grid, grid workflow scheduling, cognitive artificial intelligence

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Authors: Md. Shafiullah, Ali T. Al-Awami

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This project proposes optimal scheduling of imported power of a load aggregator with the utilization of EVs to maximize its profit. As with the increase of renewable energy resources, electricity price in competitive market becomes more uncertain and, on the other hand, with the penetration of renewable distributed generators in the distribution network the predicted load of a load aggregator also becomes uncertain in real time. Though there is uncertainties in both load and price, the use of EVs storage capacity can make the operation of load aggregator flexible. LA submits its offer to day-ahead market based on predicted loads and optimized use of its EVs to maximize its profit, as well as in real time operation it uses its energy storage capacity in such a way that it can maximize its profit. In this project, load aggregators profit maximization algorithm is formulated and the optimization problem is solved with the help of CVX. As in real time operation the forecasted loads differ from actual load, the mismatches are settled in real time balancing market. Simulation results compare the profit of a load aggregator with a hypothetical group of 1000 EVs and without EVs.

Keywords: CVX, electricity market, load aggregator, load and price uncertainties, profit maximization, real time balancing operation

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Authors: Kalpana Jamdhade, Anita Chorey, Bharti Tijare, V. M. Bhale

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A field experiment was conducted during summer season of 2011 at Agronomy research farm, Dr. PDKV, Akola, to study the effect of irrigation regime and nitrogen levels on growth and productivity of summer sesame. The experiment was laid out in split plot Design in which three irrigation scheduling on the basis of IW/CPE ratio viz., irrigation at 0.6, 0.8 and 1.0 IW/CPE ratios (I1, I2 and I3, respectively) and one irrigation scheduling based on critical growth stages of sesame (I4), in main plot and three nitrogen levels 0, 30 and 60 kg N ha-1 (N0, N1 and N2, respectively) in subplot. The result showed that plant height, number of leaves plant-1, leaf area and dry matter accumulation were maximum in irrigation scheduling at 1.0 IW/CPE ratio, which significantly superior over 0.6 IW/CPE ratio and irrigation at critical growth stages but were statistically at par with irrigation at 0.8 IW/CPE ratio. Nitrogen levels, application of 60 kg N ha-1 was recorded significantly superior all growth parameters over treatment 30 kg N ha-1 and 0 kg N ha-1. In case of yield attributes viz., No. of capsules plant-1, Test wt., grain yield and Stalk yield (qha-1) were maximum in irrigation scheduling at 1.0 IW/CPE ratio and were significantly superior over 0.8 IW/CPE ratio, 0.6 IW/CPE ratio and irrigation at critical growth stages. Application of 60 kg N ha-1 increased all yield attributing characters over application of 30 and 0 kg N ha-1. In case of economics of crop same trend was found and the highest B:C ration was obtained in irrigation scheduling at 1.0 IW/CPE ratio. Whereas, application of 30 kg N ha-1 was recorded highest B:C ration over application of 60 and 0 kg N ha-1. Interaction effect of irrigation and nitrogen levels were found to be non significant in summer season.

Keywords: irrigation regimes, nitrogen levels, summer sesame, agricultural technology

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